Code for Correlations of State of the Academy 2022

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Description: This notebook contains the code used to find correlations in the SOTA 2022. The code is documented and shared to ensure full transparency of the process data is engineered and filtered. All relevant data and information is removed to ensure complete privacy of survey respondents.

Author: Chang Dakota Sum Kiu (schang23@andover.edu, dakotacsk@protonmail.com)

Editors: Raina Yang (ryang24@andover.edu)

Last Edited: 17-05-2022

Code for Correlations of State of The Academy 2022

Use this to find specific questions from survey (search)

What is your race? Check all that apply.

array([nan, 'Asian'], dtype=object)

array(['3 hours', '8 hours', nan, '4 hours', '5 hours', '2 hours',
       '6 hours', '10 hours or more', '1 hour or less', '7 hours',
       '9 hours'], dtype=object)

Separating df by commonly used classifications

---

temp = df['#######'] for each in temp: print('df_' +each+ "= df[df['#######'] =='" + each + "']")

By class

/Users/dcoder/miniconda3/lib/python3.9/site-packages/pandas/core/frame.py:4906: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

By day or boarding

By sec

International

By gender identity

Community Type

Legacy

Political Affiliation

df_Libertarian
df_Unsure
df_Liberal
df_Socialist
df_Independent
df_nan
df_Conservative
df_Other (please specify)
df_Communist

Varsity Athlete

Changed political affiliation

Informed about Politics

df_Moderately informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Moderately informed']
df_Slightly informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Slightly informed']
df_Extremely informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Extremely informed']
df_Very informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Very informed']
df_nan = df[df['How informed do you believe you are about politics and/or current events?'] =='nan']
df_Not at all informed = df[df['How informed do you believe you are about politics and/or current events?'] =='Not at all informed']

Know Someone Incarcerated?

Leadership?

Counselor

Hooked up?

Happy?

Mental Health Diagnosis

Anxiety?

Drug use

Study Drug Use

Pressured to do Drugs

Has fake id

Has learning disability

YAY I CLEANED THE MULTI-ANSWER DATA HERES HOW:

df = df[df["Unnamed: 22"] =="Helicopter"]
df = df[df["Unnamed: 24"] =="nan"]
df = df[df["Unnamed: 25"] =="She/her/hers"]
df = df[df["Unnamed: 26"] =="They/them/theirs"]
df = df[df["Unnamed: 27"] =="Ze/hir/hirs"]
df = df[df["Unnamed: 28"] =="Unsure"]
df = df[df["Unnamed: 29"] =="Spavik"]
df = df[df["Unnamed: 31"] =="Bisexual – sexually attracted to more than one gender"]
df = df[df["Unnamed: 32"] =="Demisexual – sexually attracted only after developing an emotional connection"]
df = df[df["Unnamed: 33"] =="nan"]
df = df[df["Unnamed: 34"] =="Homosexual - sexually attracted to the same sex or gender"]
df = df[df["Unnamed: 35"] =="Pansexual - sexually attracted to others, regardless of sex or gender"]
df = df[df["Unnamed: 36"] =="Queer – an umbrella term used to describe a sexual orientation that does not conform to dominant societal norms"]
df = df[df["Unnamed: 37"] =="Questioning"]
df = df[df["Unnamed: 38"] =="Titties, Pussy"]
df = df[df["Unnamed: 40"] =="Biromantic – romantically attracted to more than one gender"]
df = df[df["Unnamed: 41"] =="Demiromantic – romantically attracted only after developing an emotional connection"]
df = df[df["Unnamed: 42"] =="nan"]
df = df[df["Unnamed: 43"] =="Homoromantic – romantically attracted to the same sex or gender"]
df = df[df["Unnamed: 44"] =="Panromantic – romantically attracted to others, regardless of sex or gender"]
df = df[df["Unnamed: 45"] =="Queer"]
df = df[df["Unnamed: 46"] =="Questioning"]
df = df[df["Unnamed: 47"] =="tall people six feet over"]
df = df[df["Unnamed: 49"] =="Black"]
df = df[df["Unnamed: 50"] =="Native Hawaiian or Pacific Islander"]
df = df[df["Unnamed: 51"] =="Indigenous Peoples of the Americas"]
df = df[df["Unnamed: 52"] =="nan"]
df = df[df["Unnamed: 54"] =="Asian American"]
df = df[df["Unnamed: 55"] =="Broadly European"]
df = df[df["Unnamed: 56"] =="East Asian"]
df = df[df["Unnamed: 57"] =="East European"]
df = df[df["Unnamed: 58"] =="Ethnically Jewish"]
df = df[df["Unnamed: 59"] =="Hispanic"]
df = df[df["Unnamed: 60"] =="Latinx"]
df = df[df["Unnamed: 61"] =="Mediterranean"]
df = df[df["Unnamed: 62"] =="Middle Eastern"]
df = df[df["Unnamed: 63"] =="Native American"]
df = df[df["Unnamed: 64"] =="North African"]
df = df[df["Unnamed: 65"] =="Pacific Islander"]
df = df[df["Unnamed: 66"] =="Scandinavian"]
df = df[df["Unnamed: 67"] =="South Asian"]
df = df[df["Unnamed: 68"] =="Southeast Asian"]
df = df[df["Unnamed: 69"] =="Sub-Saharan African"]
df = df[df["Unnamed: 70"] =="West Indian/Caribbean"]
df = df[df["Unnamed: 71"] =="nan"]
df = df[df["Unnamed: 72"] =="Other"]
df = df[df["Unnamed: 74"] =="Atheism"]
df = df[df["Unnamed: 75"] =="Buddhism"]
df = df[df["Unnamed: 76"] =="Catholicism"]
df = df[df["Unnamed: 77"] =="Christianity"]
df = df[df["Unnamed: 78"] =="Hinduism"]
df = df[df["Unnamed: 79"] =="Islam"]
df = df[df["Unnamed: 80"] =="Judaism"]
df = df[df["Unnamed: 81"] =="Mormonism"]
df = df[df["Unnamed: 82"] =="Paganism"]
df = df[df["Unnamed: 83"] =="Protestantism"]
df = df[df["Unnamed: 84"] =="nan"]
df = df[df["Unnamed: 85"] =="I am kind of questioning my religion so I will still say that I’m Christian because I’ve identified with Christianity all my life but I still acknowledge that I’m going through a period of religious reflection and discovery."]
df = df[df["Unnamed: 89"] =="Yes, from Andover"]
df = df[df["Unnamed: 90"] =="nan"]
df = df[df["Unnamed: 93"] =="I’m definitely left leaning, but I haven’t done enough research on political affiliations to be comfortable aligning with any yet."]
df = df[df["Unnamed: 99"] =="AP"]
df = df[df["Unnamed: 100"] =="BBC"]
df = df[df["Unnamed: 101"] =="Buzzfeed"]
df = df[df["Unnamed: 102"] =="CNBC"]
df = df[df["Unnamed: 103"] =="CNN"]
df = df[df["Unnamed: 104"] =="Daily Mail"]
df = df[df["Unnamed: 105"] =="Financial Times"]
df = df[df["Unnamed: 106"] =="Forbes"]
df = df[df["Unnamed: 107"] =="nan"]
df = df[df["Unnamed: 108"] =="New York Post"]
df = df[df["Unnamed: 109"] =="NPR"]
df = df[df["Unnamed: 110"] =="The Guardian"]
df = df[df["Unnamed: 111"] =="nan"]
df = df[df["Unnamed: 112"] =="nan"]
df = df[df["Unnamed: 113"] =="The Washington Post"]
df = df[df["Unnamed: 114"] =="Vox Media"]
df = df[df["Unnamed: 115"] =="Not applicable (I don't read the news)"]
df = df[df["Unnamed: 116"] =="phillipian"]
df = df[df["Unnamed: 118"] =="Magazines"]
df = df[df["Unnamed: 119"] =="Newspapers in print"]
df = df[df["Unnamed: 120"] =="nan"]
df = df[df["Unnamed: 121"] =="Podcasts/radio shows"]
df = df[df["Unnamed: 122"] =="nan"]
df = df[df["Unnamed: 123"] =="Television networks"]
df = df[df["Unnamed: 124"] =="None"]
df = df[df["Unnamed: 141"] =="Facebook"]
df = df[df["Unnamed: 142"] =="nan"]
df = df[df["Unnamed: 143"] =="Reddit"]
df = df[df["Unnamed: 144"] =="Pinterest"]
df = df[df["Unnamed: 145"] =="nan"]
df = df[df["Unnamed: 146"] =="TikTok"]
df = df[df["Unnamed: 147"] =="Twitter"]
df = df[df["Unnamed: 148"] =="Tinder"]
df = df[df["Unnamed: 149"] =="Tumblr"]
df = df[df["Unnamed: 150"] =="VSCO"]
df = df[df["Unnamed: 151"] =="WeChat"]
df = df[df["Unnamed: 152"] =="Not applicable"]
df = df[df["Unnamed: 153"] =="Youtube"]
df = df[df["Unnamed: 158"] =="Gluten free"]
df = df[df["Unnamed: 159"] =="Lactose Intolerant"]
df = df[df["Unnamed: 160"] =="Pescetarian"]
df = df[df["Unnamed: 161"] =="Religious (i.e. Kosher, Halal, etc.)"]
df = df[df["Unnamed: 162"] =="Vegan"]
df = df[df["Unnamed: 163"] =="Vegetarian"]
df = df[df["Unnamed: 164"] =="None"]
df = df[df["Unnamed: 165"] =="Egg fish allergy"]
df = df[df["Unnamed: 183"] =="nan"]
df = df[df["Unnamed: 184"] =="nan"]
df = df[df["Unnamed: 185"] =="nan"]
df = df[df["Unnamed: 186"] =="I do not have a support system on campus"]
df = df[df["Unnamed: 187"] =="Prayer"]
df = df[df["Unnamed: 191"] =="Digital (i.e. fingering, hand jobs, etc.)"]
df = df[df["Unnamed: 192"] =="Oral"]
df = df[df["Unnamed: 193"] =="Vaginal"]
df = df[df["Unnamed: 194"] =="nan"]
df = df[df["Unnamed: 204"] =="Yes, condoms"]
df = df[df["Unnamed: 205"] =="Yes, finger condoms"]
df = df[df["Unnamed: 206"] =="Yes, dental dams"]
df = df[df["Unnamed: 207"] =="Yes, other"]
df = df[df["Unnamed: 208"] =="No"]
df = df[df["Unnamed: 209"] =="nan"]
df = df[df["Unnamed: 234"] =="DXM"]
df = df[df["Unnamed: 235"] =="DMT"]
df = df[df["Unnamed: 236"] =="Heroin"]
df = df[df["Unnamed: 237"] =="Ketamine"]
df = df[df["Unnamed: 238"] =="LSD"]
df = df[df["Unnamed: 239"] =="Mescaline peyote"]
df = df[df["Unnamed: 240"] =="Methamphetamine"]
df = df[df["Unnamed: 241"] =="MDMA (Molly)"]
df = df[df["Unnamed: 242"] =="Opioids"]
df = df[df["Unnamed: 243"] =="Psilocybin mushrooms"]
df = df[df["Unnamed: 244"] =="Other"]
df = df[df["Unnamed: 245"] =="nan"]
df = df[df["Unnamed: 250"] =="School-sponsored event"]
df = df[df["Unnamed: 251"] =="Classroom setting"]
df = df[df["Unnamed: 252"] =="Other on-campus setting"]
df = df[df["Unnamed: 253"] =="nan"]
df = df[df["Unnamed: 261"] =="Computer Science"]
df = df[df["Unnamed: 262"] =="nan"]
df = df[df["Unnamed: 263"] =="nan"]
df = df[df["Unnamed: 264"] =="Mathematics"]
df = df[df["Unnamed: 265"] =="Music"]
df = df[df["Unnamed: 266"] =="Natural Sciences"]
df = df[df["Unnamed: 267"] =="nan"]
df = df[df["Unnamed: 268"] =="Physical Education"]
df = df[df["Unnamed: 269"] =="Statistics"]
df = df[df["Unnamed: 270"] =="Theatre and Dance"]
df = df[df["Unnamed: 271"] =="World Languages"]
df = df[df["Unnamed: 272"] =="Not applicable (No unreasonable grading disparities exist at Andover)"]
df = df[df["Unnamed: 297"] =="nan"]
df = df[df["Unnamed: 298"] =="nan"]
df = df[df["Unnamed: 299"] =="Gender"]
df = df[df["Unnamed: 300"] =="Neurodivergence"]
df = df[df["Unnamed: 301"] =="nan"]
df = df[df["Unnamed: 302"] =="Race"]
df = df[df["Unnamed: 303"] =="nan"]
df = df[df["Unnamed: 304"] =="Sexual orientation"]
df = df[df["Unnamed: 305"] =="Socioeconomic status"]
df = df[df["Unnamed: 306"] =="I do not think that faculty hire should be based on diversity"]
df = df[df["Unnamed: 308"] =="nan"]
df = df[df["Unnamed: 309"] =="nan"]
df = df[df["Unnamed: 310"] =="nan"]
df = df[df["Unnamed: 311"] =="Club advisors"]
df = df[df["Unnamed: 312"] =="unsure of the question "]
df = df[df["Unnamed: 314"] =="Ability/Disability"]
df = df[df["Unnamed: 315"] =="Ethnicity"]
df = df[df["Unnamed: 316"] =="Gender"]
df = df[df["Unnamed: 317"] =="Neurodiversities"]
df = df[df["Unnamed: 318"] =="nan"]
df = df[df["Unnamed: 319"] =="Race"]
df = df[df["Unnamed: 320"] =="Religion"]
df = df[df["Unnamed: 321"] =="Sexual orientation"]
df = df[df["Unnamed: 322"] =="Socioeconomic status"]
df = df[df["Unnamed: 323"] =="Unsure "]
df = df[df["Unnamed: 325"] =="nan"]
df = df[df["Unnamed: 326"] =="nan"]
df = df[df["Unnamed: 327"] =="Gender"]
df = df[df["Unnamed: 328"] =="nan"]
df = df[df["Unnamed: 329"] =="nan"]
df = df[df["Unnamed: 330"] =="Race"]
df = df[df["Unnamed: 331"] =="Religion"]
df = df[df["Unnamed: 332"] =="Sexual orientation"]
df = df[df["Unnamed: 333"] =="Socioeconomic status"]
df = df[df["Unnamed: 334"] =="Connections"]
df = df[df["Unnamed: 336"] =="Ability/Disability"]
df = df[df["Unnamed: 337"] =="Ethnicity"]
df = df[df["Unnamed: 338"] =="Gender"]
df = df[df["Unnamed: 339"] =="Neurodivergence"]
df = df[df["Unnamed: 340"] =="Political beliefs"]
df = df[df["Unnamed: 341"] =="Race"]
df = df[df["Unnamed: 342"] =="Religion"]
df = df[df["Unnamed: 343"] =="Sexual orientation"]
df = df[df["Unnamed: 344"] =="Socioeconomic status"]
df = df[df["Unnamed: 345"] =="anxiety"]
df = df[df["Unnamed: 347"] =="Ability/Disability"]
df = df[df["Unnamed: 348"] =="Ethnicity"]
df = df[df["Unnamed: 349"] =="Gender"]
df = df[df["Unnamed: 350"] =="Neurodivergence"]
df = df[df["Unnamed: 351"] =="nan"]
df = df[df["Unnamed: 352"] =="Race"]
df = df[df["Unnamed: 353"] =="Religion"]
df = df[df["Unnamed: 354"] =="Sexual orientation"]
df = df[df["Unnamed: 355"] =="Socioeconomic status"]
df = df[df["Unnamed: 357"] =="Ability/Disability"]
df = df[df["Unnamed: 358"] =="Ethnicity"]
df = df[df["Unnamed: 359"] =="Gender identity"]
df = df[df["Unnamed: 360"] =="Neurodivergence"]
df = df[df["Unnamed: 361"] =="Political belief"]
df = df[df["Unnamed: 362"] =="Race"]
df = df[df["Unnamed: 363"] =="Religion"]
df = df[df["Unnamed: 364"] =="Sexual orientation"]
df = df[df["Unnamed: 365"] =="Socioeconomic status"]
df = df[df["Unnamed: 366"] =="body image"]
df = df[df["Unnamed: 372"] =="Warning"]
df = df[df["Unnamed: 373"] =="Probation"]
df = df[df["Unnamed: 374"] =="nan"]

IMPORTANT: function to find intersection between multi-answer questions

Correlation for Race

Correlation for Sexuality

Correlation for Social Divide

Feel free to add more if i missed some!

---

Charts by Sections According to Past SOTAs

General

SEC by Percieved SEC

GPA by Income

SUGGESTION (DC): INTERNATIONAL VS SEC AND POLITICS

Legacy v Income

Sexual Orientation vs Gender Identity

bisexaul out of she/her: 113 out of 471 , percentage : 23.991507430997878 %
demisexaul out of she/her: 8 out of 471 , percentage : 1.6985138004246285 %
heterosexual out of she/her: 276 out of 471 , percentage : 58.59872611464968 %
homosexual out of she/her: 19 out of 471 , percentage : 4.033970276008493 %
pansexual out of she/her: 17 out of 471 , percentage : 3.6093418259023355 %
queer out of she/her: 47 out of 471 , percentage : 9.978768577494693 %
questioning out of she/her: 52 out of 471 , percentage : 11.040339702760086 %

bisexaul out of he/him: 36 out of 446 , percentage : 8.071748878923767 %
demisexaul out of he/him: 5 out of 446 , percentage : 1.1210762331838564 %
heterosexual out of he/him: 370 out of 446 , percentage : 82.95964125560538 %
homosexual out of he/him: 22 out of 446 , percentage : 4.932735426008969 %
pansexual out of he/him: 7 out of 446 , percentage : 1.5695067264573992 %
queer out of he/him: 12 out of 446 , percentage : 2.690582959641256 %
questioning out of he/him: 10 out of 446 , percentage : 2.242152466367713 %

Varsity v SEC

Varsity vs Race

df_Asian : 380
No     212
Yes    168
Name: Are you a varsity athlete?, dtype: int64

df_Black : 110
No     60
Yes    50
Name: Are you a varsity athlete?, dtype: int64

df_NHPI : 12
Yes    7
No     5
Name: Are you a varsity athlete?, dtype: int64

df_Indigenous : 26
No     18
Yes     8
Name: Are you a varsity athlete?, dtype: int64

df_White : 543
Yes    284
No     259
Name: Are you a varsity athlete?, dtype: int64

Class vs Class of Partner

---

Politics and Worldview

Political affiliation and gender identity

Varsity athletics and political affiliation

Have your political beliefs cahnged and current political affiliation

Mention of political affiliation and current events knowledge

Political Orientation vs Censorship

Affirmative Action

Affirmative Action by Race

df_Asian : 380
Yes    285
No      73
Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64

df_Black : 110
Yes    100
No       6
Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64

df_NHPI : 12
Yes    8
No     3
Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64

df_Indigenous : 26
Yes    24
No      1
Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64

df_White : 543
Yes    423
No      89
Name: Do you support affirmative action in academic institutions—“positive steps taken to increase the representation of women and minorities in areas of employment, education, and culture from which they have been historically excluded” (Stanford University)?, dtype: int64

Affirmative Action by Gender Identity

FEMINISM BY GENDER IDENTITY

Reverse Racism

df_Asian : 380
No     245
Yes    116
Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64

df_Black : 110
No     95
Yes    11
Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64

df_NHPI : 12
No     8
Yes    3
Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64

df_Indigenous : 26
No     19
Yes     6
Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64

df_White : 543
No     335
Yes    177
Name: Racism is defined as “the systemic subordination of members of targeted racial groups who have relatively little social power” (Vanderbilt University). Do you believe that white people can experience racism (colloquially known as reverse racism)?, dtype: int64

Abortion Rights vs Gender

Prison System

Decriminalization drug x incarcerated

Household own Firearm vs Area

Firearm Ownership x political

Firearm by Income

Student Leadership by Political Affiliation

---

Wellness

Body Insecurity by Gender

Body Insecurity by Varsity

Sykes vs Support enough

Happy vs Income

Happy vs Percieved SEC

Happy vs GPA

Sykes mental health support and experience with mental illness

Anxiety and Gender Identity

4 hours             248
3 hours             207
5 hours             161
2 hours              83
6 hours              71
7 hours              25
1 hour or less       19
8 hours              15
10 hours or more     15
9 hours               3
Name: How many hours do you spend on coursework outside of class each day?, dtype: int64

3.662133891213389

---

Sex

Sex Ed over the years

Substance consumption and Sex

Rape Culture and Gender Identity

Masturbation and Gender Identity

Peer pressure and Hook Up culture

Hookup vs sexual orientation

df_bi : 156
Yes    128
No      18
Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64

df_demi : 18
Yes    15
No      1
Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64

df_het : 653
Yes    455
No     134
Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64

df_homo : 47
Yes    37
No      6
Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64

df_pan : 31
Yes    26
No      4
Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64

df_queer : 71
Yes    55
No      8
Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64

df_questioning : 64
Yes    54
No      8
Name: Do you believe that there is a hook-up culture at Andover?, dtype: int64

Hookup and Gender

Sex and Class

2022 and anal: 21 out of 238 , percentage : 8.823529411764707 %
2023 and anal: 7 out of 265 , percentage : 2.6415094339622645 %
2024 and anal: 7 out of 265 , percentage : 2.6415094339622645 %
2025 and anal: 1 out of 184 , percentage : 0.5434782608695652 %

2022 and digital: 121 out of 238 , percentage : 50.84033613445378 %
2023 and digital: 97 out of 265 , percentage : 36.60377358490566 %
2024 and digital: 66 out of 265 , percentage : 24.90566037735849 %
2025 and digital: 20 out of 184 , percentage : 10.869565217391305 %

2022 and oral: 119 out of 238 , percentage : 50.0 %
2023 and oral: 88 out of 265 , percentage : 33.20754716981132 %
2024 and oral: 61 out of 265 , percentage : 23.0188679245283 %
2025 and oral: 14 out of 184 , percentage : 7.608695652173914 %

2022 and vaginal: 93 out of 238 , percentage : 39.075630252100844 %
2023 and vaginal: 57 out of 265 , percentage : 21.50943396226415 %
2024 and vaginal: 31 out of 265 , percentage : 11.69811320754717 %
2025 and vaginal: 7 out of 184 , percentage : 3.804347826086957 %

Sexuality and Frequency of Sex

df_bi : 156
Not applicable, I have never engaged in sexual activity    80
Several times a week                                       16
Weekly                                                     16
Several times a year                                       15
About once per month                                       10
Once a year or less                                         9
Name: How frequently, on average, do you engage in sexual activity?, dtype: int64

df_demi : 18
Not applicable, I have never engaged in sexual activity    11
Several times a year                                        2
Weekly                                                      2
Several times a week                                        1
Once a year or less                                         1
Name: How frequently, on average, do you engage in sexual activity?, dtype: int64

df_het : 653
Not applicable, I have never engaged in sexual activity    348
Weekly                                                      60
Several times a year                                        60
About once per month                                        45
Several times a week                                        44
Once a year or less                                         22
Daily                                                       12
Name: How frequently, on average, do you engage in sexual activity?, dtype: int64

df_homo : 47
Not applicable, I have never engaged in sexual activity    31
Several times a week                                        4
Several times a year                                        3
Once a year or less                                         2
Weekly                                                      2
About once per month                                        1
Name: How frequently, on average, do you engage in sexual activity?, dtype: int64

df_pan : 31
Not applicable, I have never engaged in sexual activity    18
Once a year or less                                         4
Several times a week                                        3
About once per month                                        3
Several times a year                                        2
Name: How frequently, on average, do you engage in sexual activity?, dtype: int64

df_queer : 71
Not applicable, I have never engaged in sexual activity    39
Once a year or less                                         7
About once per month                                        6
Several times a year                                        5
Several times a week                                        4
Weekly                                                      3
Name: How frequently, on average, do you engage in sexual activity?, dtype: int64

df_questioning : 64
Not applicable, I have never engaged in sexual activity    43
About once per month                                        7
Weekly                                                      4
Once a year or less                                         3
Several times a year                                        2
Daily                                                       2
Several times a week                                        2
Name: How frequently, on average, do you engage in sexual activity?, dtype: int64

---

Drugs and Alc

Normalization of drug vs drugs freq

Study Drugs vs GPA

Pressure to do Drugs + Normalization

Leadership and drugs

Fake id and Drug Use

---

School

GPA and Gender

GPA and Varsity

Considering Leaving vs Year

Andover Affecting College by Year

Accomodations vs Learning Disability

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Campus Culture

Andover fossil fuel divestment and political affiliation

Ideological Diversity and Political Orientationa

Anti Racism Work v Class

Anti Racism Work v Political Orientation

Anti-racism work by race

df_Asian : 380
No          154
Somewhat    147
Yes          41
Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64

df_Black : 110
No          55
Somewhat    41
Yes          4
Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64

df_NHPI : 12
Somewhat    3
Yes         3
No          3
Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64

df_Indigenous : 26
No          14
Somewhat     8
Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64

df_White : 543
Somewhat    185
No          172
Yes         122
Name: Do you think Andover’s anti-racist work is sufficient?, dtype: int64

Andover glorifying athletics and varsity athlete

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Campus Diversity

Faculty lacking in gender diversity and gender identity

Number of NB people who think faculty gender diversity is not enough: 7 out of 17 , percentage : 41.17647058823529 %
Number of Men who think faculty gender diversity is not enough: 45 out of 426 , percentage : 10.56338028169014 %
Number of Women who think faculty gender diversity is not enough: 90 out of 458 , percentage : 19.65065502183406 %

Faculty lacking in racial diversity vs race

Number of df_Asian who think faculty racial diversity is not enough: 140 out of 380 , percentage : 36.84210526315789 %

Number of df_Black who think faculty racial diversity is not enough: 74 out of 110 , percentage : 67.27272727272727 %

Number of df_NHPI who think faculty racial diversity is not enough: 3 out of 12 , percentage : 25.0 %

Number of df_Indigenous who think faculty racial diversity is not enough: 18 out of 26 , percentage : 69.23076923076923 %

Number of df_White who think faculty racial diversity is not enough: 167 out of 543 , percentage : 30.755064456721914 %

Social divide due to socioeconomic status and perceived socioeconomic class

df_socialDivideNo : 49
Upper-middle class    19
Upper class           16
Middle class          11
Lower-middle class     3
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDivideAbility : 225
Upper-middle class    93
Upper class           66
Middle class          38
Lower class           14
Lower-middle class    14
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDivideEthnicity : 420
Upper-middle class    172
Upper class           126
Middle class           67
Lower-middle class     33
Lower class            21
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDivideGender : 372
Upper-middle class    158
Upper class           121
Middle class           49
Lower-middle class     25
Lower class            19
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDivideNeuroDiv : 200
Upper-middle class    76
Upper class           69
Middle class          32
Lower class           12
Lower-middle class    11
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDividePolitics : 523
Upper-middle class    219
Upper class           166
Middle class           84
Lower-middle class     31
Lower class            22
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDivideRace : 556
Upper-middle class    232
Upper class           170
Middle class           90
Lower-middle class     40
Lower class            23
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDivideReligion : 115
Upper-middle class    46
Upper class           32
Middle class          20
Lower class           10
Lower-middle class     7
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDivideSexualOrientation : 482
Upper-middle class    201
Upper class           152
Middle class           73
Lower-middle class     31
Lower class            25
Name: What is your perceived socioeconomic status?, dtype: int64

df_socialDivideSEC : 535
Upper-middle class    225
Upper class           164
Middle class           83
Lower-middle class     39
Lower class            24
Name: What is your perceived socioeconomic status?, dtype: int64

Social divide due to race and race

Number of df_Asian who think there is social divide due to race: 237 out of 380 , percentage : 62.36842105263158 %

Number of df_Black who think there is social divide due to race: 77 out of 110 , percentage : 70.0 %

Number of df_NHPI who think there is social divide due to race: 6 out of 12 , percentage : 50.0 %

Number of df_Indigenous who think there is social divide due to race: 19 out of 26 , percentage : 73.07692307692307 %

Number of df_White who think there is social divide due to race: 294 out of 543 , percentage : 54.14364640883977 %

Ability to obtain leadership positions and gender identity

Women who think gender affects ability to obtain leadership pos: 131 out of 458 , percentage : 28.602620087336245 %
Men who think gender affects ability to obtain leadership pos: 80 out of 426 , percentage : 18.779342723004692 %
NBs who think gender affects ability to obtain leadership pos: 6 out of 17 , percentage : 35.294117647058826 %

Ability to obtain leadership positions and race

Number of df_Asian who think race affects ability to obtain leadership pos: 131 out of 380 , percentage : 34.473684210526315 %

Number of df_Black who think race affects ability to obtain leadership pos: 41 out of 110 , percentage : 37.27272727272727 %

Number of df_NHPI who think race affects ability to obtain leadership pos: 0 out of 12 , percentage : 0.0 %

Number of df_Indigenous who think race affects ability to obtain leadership pos: 10 out of 26 , percentage : 38.46153846153847 %

Number of df_White who think race affects ability to obtain leadership pos: 130 out of 543 , percentage : 23.941068139963168 %

Ability to obtain leadership pos due to socioeconomic status and perceived class

Unsure Income vs ability to gain Leadership Pos IS affected by SEC: 19 out of 230 , percentage : 8.26086956521739 %
0-34k Income vs ability to gain Leadership Pos IS affected by SEC: 8 out of 27 , percentage : 29.629629629629626 %
35-59k Income vs ability to gain Leadership Pos IS affected by SEC: 9 out of 32 , percentage : 28.125 %
60-99k Income vs ability to gain Leadership Pos IS affected by SEC: 13 out of 56 , percentage : 23.214285714285715 %
100-149k Income vs ability to gain Leadership Pos IS affected by SEC: 16 out of 79 , percentage : 20.253164556962027 %
150-249k Income vs ability to gain Leadership Pos IS affected by SEC: 13 out of 117 , percentage : 11.11111111111111 %
250-499k Income vs ability to gain Leadership Pos IS affected by SEC: 24 out of 149 , percentage : 16.10738255033557 %
500+k Income vs ability to gain Leadership Pos IS affected by SEC: 40 out of 261 , percentage : 15.32567049808429 %

Level of comfort in a classroom and race

Number of df_Asian who think their race affects their comfort level in classroom: 115 out of 380 , percentage : 30.263157894736842 %

Number of df_Black who think their race affects their comfort level in classroom: 77 out of 110 , percentage : 70.0 %

Number of df_NHPI who think their race affects their comfort level in classroom: 2 out of 12 , percentage : 16.666666666666664 %

Number of df_Indigenous who think their race affects their comfort level in classroom: 14 out of 26 , percentage : 53.84615384615385 %

Number of df_White who think their race affects their comfort level in classroom: 83 out of 543 , percentage : 15.285451197053407 %

Level of comfort in classroom setting and gender identity

Women who think gender affects ability to obtain leadership pos: 160 out of 458 , percentage : 34.93449781659388 %
Men who think gender affects ability to obtain leadership pos: 49 out of 426 , percentage : 11.502347417840376 %
NBs who think gender affects ability to obtain leadership pos: 11 out of 17 , percentage : 64.70588235294117 %

EBI curriculum effectiveness and class year

Day Student integration and student status

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Discipline

DC system effectiveness and DC recipients

Major offense and boarding/day status

Cheating and class year

Boarding vs Day student understanding dc process

DC privilege and family income